Enhanced threat assessment

ABSTRACT

A computer includes a processor and a memory, the memory storing instructions executable by the processor to, in a host vehicle, determine a threat number for a first target vehicle based on data received from the first target vehicle indicating at least one of a position, speed, or route history of the first target vehicle, identify a second target vehicle based on data collected with one or more host vehicle sensors when the threat number of the first target vehicle exceeds a threshold, determine that the first target vehicle and the second target vehicle are not a same vehicle, and, then, upon determining that the first target vehicle and the second target vehicle are not the same vehicle and that at least one of (a) a barrier is detected between the host vehicle and the first target vehicle or (b) that a threat number of the second target vehicle exceeds a second threshold, suppress a collision avoidance action.

BACKGROUND

Vehicle collisions often occur at intersections of roadways. A vehiclecan detect a target vehicle at the intersection. Collision mitigationbetween the vehicle and the target vehicle may be difficult andexpensive to implement. For example, determining a threat assessment onthe target vehicle can require data from a plurality of sensors.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example system for collision avoidanceand mitigation.

FIG. 2 is a plan view of a host vehicle and an object occluding a targetvehicle.

FIG. 3 is a plan view of the host vehicle, the target vehicle, and asecond target vehicle.

FIG. 4 is a plan view of a barrier between the host vehicle and thetarget vehicle.

FIG. 5 is a plan view of the target vehicle at a different elevationthan the host vehicle.

FIGS. 6A-6B are a block diagram of an example process for collisionavoidance and mitigation.

DETAILED DESCRIPTION

A computer includes a processor and a memory, the memory storinginstructions executable by the processor to, in a host vehicle,determine a threat number for a first target vehicle based on datareceived from the first target vehicle indicating at least one of aposition, speed, or route history of the first target vehicle, identifya second target vehicle based on data collected with one or more hostvehicle sensors when the threat number of the first target vehicleexceeds a threshold, determine that the first target vehicle and thesecond target vehicle are not a same vehicle, and, then, upondetermining that the first target vehicle and the second target vehicleare not the same vehicle and that at least one of (a) a barrier isdetected between the host vehicle and the first target vehicle or (b)that a threat number of the second target vehicle exceeds a secondthreshold, suppress a collision avoidance action.

The instructions can further include instructions to determine that thefirst target vehicle and the second target vehicle are not the samevehicle when data from the sensors indicating at least one of a size,position, and speed differs from corresponding received data by athreshold.

The instructions can further include instructions to suppress thecollision avoidance action upon determining that a difference between avertical position of the first target vehicle and a vertical position ofthe host vehicle exceeds a threshold.

The instructions can further include instructions to suppress thecollision avoidance action upon determining that the first targetvehicle is on a bridge above the host vehicle.

The instructions can further include instructions to allow the collisionavoidance action upon failing to detect the second target vehicle withthe vehicle sensors.

The instructions can further include instructions to allow the collisionavoidance action upon determining that the threat number of the secondtarget vehicle is below a second threshold.

The instructions can further include instructions to arbitrate thecollision avoidance action and a second collision avoidance action, thesecond collision avoidance action directed toward the second targetvehicle, based on a receipt order of the received data from the firsttarget vehicle and the sensor data identifying the second targetvehicle.

The instructions can further include instructions to suppress thecollision avoidance action upon identifying that the first targetvehicle and the second target vehicle are the same vehicle and detectingno occluding object between host vehicle and the first target vehicle.

The instructions can further include instructions to allow the collisionavoidance action upon identifying an occluding object between the hostvehicle and the first target vehicle.

The instructions can further include instructions to suppress thecollision avoidance action upon determining that the second targetvehicle is closer to the host vehicle than the first target vehicle.

A method includes determining a threat number for a first target vehiclebased on data received from the first target vehicle indicating at leastone of a position, speed, or route history of the first target vehicle,identifying a second target vehicle based on data collected with one ormore host vehicle sensors when the threat number of the first targetvehicle exceeds a threshold, determining that the first target vehicleand the second target vehicle are not a same vehicle, and, then, upondetermining that the first target vehicle and the second target vehicleare not the same vehicle and that at least one of (a) a barrier isdetected between a host vehicle and the first target vehicle or (b) thata threat number of the second target vehicle exceeds a second threshold,suppressing a collision avoidance action.

The method can further include determining that the first target vehicleand the second target vehicle are not the same vehicle when data fromthe sensors indicating at least one of a size, position, and speeddiffers from corresponding received data by a threshold.

The method can further include suppressing the collision avoidanceaction upon determining that a difference between a vertical position ofthe first target vehicle and a vertical position of the host vehicleexceeds a threshold.

The method can further include suppressing the collision avoidanceaction upon determining that the first target vehicle is on a bridgeabove the host vehicle.

The method can further include allowing the collision avoidance actionupon failing to detect the second target vehicle with the vehiclesensors.

The method can further include allowing the collision avoidance actionupon determining that the threat number of the second target vehicle isbelow a second threshold.

The method can further include arbitrating the collision avoidanceaction and a second collision avoidance action, the second collisionavoidance action directed toward the second target vehicle, based on areceipt order of the received data from the first target vehicle and thesensor data identifying the second target vehicle.

The method can further include suppressing the collision avoidanceaction upon identifying that the first target vehicle and the secondtarget vehicle are the same vehicle and detecting no occluding objectbetween host vehicle and the first target vehicle.

The method can further include allowing the collision avoidance actionupon identifying an occluding object between the host vehicle and thefirst target vehicle.

The method can further include suppressing the collision avoidanceaction upon determining that the second target vehicle is closer to thehost vehicle than the first target vehicle.

A system includes a host vehicle sensor, means for determining a threatnumber for a first target vehicle based on data received from the firsttarget vehicle indicating at least one of a position, speed, or routehistory of the first target vehicle, means for identifying a secondtarget vehicle based on data collected with the host vehicle sensor whenthe threat number of the first target vehicle exceeds a threshold, meansfor determining that the first target vehicle and the second targetvehicle are not a same vehicle, and means for suppressing a collisionavoidance action upon determining that the first target vehicle and thesecond target vehicle are not the same vehicle and that at least one of(a) a barrier is detected between a host vehicle and the first targetvehicle or (b) that a threat number of the second target vehicle exceedsa second threshold.

The system can further include means for determining that the firsttarget vehicle and the second target vehicle are not the same vehiclewhen data from the sensors indicating at least one of a size, position,and speed differs from corresponding received data by a threshold.

The system can further include means for allowing the collisionavoidance action upon determining that the threat number of the secondtarget vehicle is below the second threshold.

The system can further include means for arbitrating the collisionavoidance action and a second collision avoidance action, the secondcollision avoidance action directed toward the second target vehicle,based on a receipt order of the received data from the first targetvehicle and the sensor data identifying the second target vehicle.

The system can further include means for suppressing the collisionavoidance action upon identifying that the first target vehicle and thesecond target vehicle are the same vehicle and detecting no occludingobject between host vehicle and the first target vehicle.

Further disclosed is a computing device programmed to execute any of theabove method steps. Yet further disclosed is a vehicle comprising thecomputing device. Yet further disclosed is a computer program product,comprising a computer readable medium storing instructions executable bya computer processor, to execute any of the above method steps.

Upon detecting target vehicles, a host vehicle can generate collisionavoidance actions to avoid and/or mitigate a collision with one or moreof the target vehicles. However, not all target vehicles may warrantcollision avoidance. For example, the target vehicle may be separatedfrom the host vehicle by a concrete barrier, and the target vehicle maythus not be likely to collide with the host vehicle. By suppressingcollision avoidance actions for target vehicles not likely to collidewith the host vehicle, the computer can reduce a number of nuisanceactions. Furthermore, the system can differentiate between targetvehicles that can allow a human operator to react, during which acollision avoidance warning can be suppressed, and potential collisionswith target vehicles not visible to the human operator, during which thecomputer can perform the collision avoidance action to avoid and/ormitigate a potential collision.

FIG. 1 illustrates an example system 100 for collision avoidance andmitigation. Unless indicated otherwise in this disclosure, an“intersection” is defined as a location where two or more vehicles'respective current or potential future trajectories cross. Thus, anintersection could be at any location on a surface where two or morevehicles could collide, e.g. a road, a driveway, a parking lot, anentrance to a public road, driving paths, etc. Accordingly, anintersection is determined by identifying a location where two or morevehicles may meet, i.e., collide. Such determination uses potentialfuture trajectories of a host vehicle 101 as well as nearby othervehicles and/or other objects.

A computer 105 in the vehicle 101 is programmed to receive collecteddata 115 from one or more sensors 110. For example, vehicle 101 data 115may include a location of the vehicle 101, data about an environmentaround a vehicle, data about an object outside the vehicle such asanother vehicle, etc. A vehicle 101 location is typically provided in aconventional form, e.g., geo-coordinates such as latitude and longitudecoordinates obtained via a navigation system that uses the GlobalPositioning System (GPS). Further examples of data 115 can includemeasurements of vehicle 101 systems and components, e.g., a vehicle 101velocity, a vehicle 101 trajectory, etc.

The computer 105 is generally programmed for communications on a vehicle101 network, e.g., including a conventional vehicle 101 communicationsbus. Via the network, bus, and/or other wired or wireless mechanisms(e.g., a wired or wireless local area network in the vehicle 101), thecomputer 105 may transmit messages to various devices in a vehicle 101and/or receive messages from the various devices, e.g., controllers,actuators, sensors, etc., including sensors 110. Alternatively oradditionally, in cases where the computer 105 actually comprisesmultiple devices, the vehicle network may be used for communicationsbetween devices represented as the computer 105 in this disclosure. Inaddition, the computer 105 may be programmed for communicating with thenetwork 125, which, as described below, may include various wired and/orwireless networking technologies, e.g., cellular, Bluetooth®, Bluetooth®Low Energy (BLE), wired and/or wireless packet networks, etc.

The data store 106 can be of any type, e.g., hard disk drives, solidstate drives, servers, or any volatile or non-volatile media. The datastore 106 can store the collected data 115 sent from the sensors 110.

Sensors 110 can include a variety of devices. For example, variouscontrollers in a vehicle 101 may operate as sensors 110 to provide data115 via the vehicle 101 network or bus, e.g., data 115 relating tovehicle speed, acceleration, position, subsystem and/or componentstatus, etc. Further, other sensors 110 could include cameras, motiondetectors, etc., i.e., sensors 110 to provide data 115 for evaluating aposition of a component, evaluating a slope of a roadway, etc. Thesensors 110 could, without limitation, also include short range radar,long range radar, LIDAR, and/or ultrasonic transducers.

Collected data 115 can include a variety of data collected in a vehicle101. Examples of collected data 115 are provided above, and moreover,data 115 are generally collected using one or more sensors 110, and mayadditionally include data calculated therefrom in the computer 105,and/or at the server 130. In general, collected data 115 may include anydata that may be gathered by the sensors 110 and/or computed from suchdata.

The vehicle 101 can include a plurality of vehicle components 120. Inthis context, each vehicle component 120 includes one or more hardwarecomponents adapted to perform a mechanical function or operation—such asmoving the vehicle 101, slowing or stopping the vehicle 101, steeringthe vehicle 101, etc. Non-limiting examples of components 120 include apropulsion component (that includes, e.g., an internal combustion engineand/or an electric motor, etc.), a transmission component, a steeringcomponent (e.g., that may include one or more of a steering wheel, asteering rack, etc.), a brake component, a park assist component, anadaptive cruise control component, an adaptive steering component, amovable seat, and the like.

When the computer 105 operates the vehicle 101, the vehicle 101 is an“autonomous” vehicle 101. For purposes of this disclosure, the term“autonomous vehicle” is used to refer to a vehicle 101 operating in afully autonomous mode. A fully autonomous mode is defined as one inwhich each of vehicle 101 propulsion (typically via a powertrainincluding an electric motor and/or internal combustion engine), braking,and steering are controlled by the computer 105. A semi-autonomous modeis one in which at least one of vehicle 101 propulsion (typically via apowertrain including an electric motor and/or internal combustionengine), braking, and steering are controlled at least partly by thecomputer 105 as opposed to a human operator. In a non-autonomous mode,i.e., a manual mode, the vehicle 101 propulsion, braking, and steeringare controlled by the human operator.

The system 100 can further include a network 125 connected to a server130 and a data store 135. The computer 105 can further be programmed tocommunicate with one or more remote sites such as the server 130, viathe network 125, such remote site possibly including a data store 135.The network 125 represents one or more mechanisms by which a vehiclecomputer 105 may communicate with a remote server 130. Accordingly, thenetwork 125 can be one or more of various wired or wirelesscommunication mechanisms, including any desired combination of wired(e.g., cable and fiber) and/or wireless (e.g., cellular, wireless,satellite, microwave, and radio frequency) communication mechanisms andany desired network topology (or topologies when multiple communicationmechanisms are utilized). Exemplary communication networks includewireless communication networks (e.g., using Bluetooth®, Bluetooth® LowEnergy (BLE), IEEE 802.11, vehicle-to-vehicle (V2V) such as DedicatedShort Range Communications (DSRC), etc.), local area networks (LAN)and/or wide area networks (WAN), including the Internet, providing datacommunication services.

FIG. 2 is a plan view of an example host vehicle 101 and an exampletarget vehicle 200. The target vehicle 200 can transmit a message overthe network 125 (e.g., via V2V communications) to the host vehicle 101.The message can include data 115 about the target vehicle 200, e.g., aspeed, a position, an elevation, a trajectory, a route history, etc. Thecomputer 105 can, upon receiving the message, actuate one or moresensors to detect the target vehicle 200, as described below. When thecomputer 105 detects the target vehicle 200 with sensor data 115 thatsubstantially matches the data 115 in the message, the computer 105 candetermine that the detected target vehicle 200 is the same vehicle asthe target vehicle 200 that sent the message, as described below.

The computer 105 can determine a threat number TN for the target vehicle200 based on the data 115 in the message. A threat number is aprediction of whether a specific target 200 will intersect or collidewith the host vehicle 101. Specifically, the computer 105 may determinean acceleration threat number ATN, a brake threat number BTN, and asteering threat number STN for the host vehicle 101 and the target 200,and based on the threat numbers ATN, BTN, STN, which may be combinedinto a single threat number TN, to actuate components 120.

The BTN is a measure of a needed longitudinal deceleration to allow thehost vehicle 101 to stop before colliding with the target 200. The BTNcan be based on a measured host vehicle 101 speed, a distance betweenthe target 200 and the host vehicle 101, and the projected paths 210 h,210 a. The computer 105 can determine a longitudinal deceleration tostop the host vehicle 101 before colliding with the target 200, e.g., 2m/s². The computer 105 can determine a maximum deceleration of the hostvehicle 101, e.g., 8 m/s². The BTN can be the ratio of the neededdeceleration to the maximum deceleration, e.g., BTN=2/8=0.25. If theneeded deceleration to avoid a collision with the target 200 exceeds themaximum deceleration of the host vehicle 101, i.e., BTN>1, then thecomputer 105 can set the value of the BTN to 1, i.e., if BTN>1, BTN=1.

The STN is a measure of a needed lateral acceleration to allow the hostvehicle 101 to steer away from the target 200. As with the BTN, thecomputer 105 can determine a needed lateral acceleration to avoid acollision between the host vehicle 101 and the target 200. The STN canbe the ratio of the needed lateral acceleration to a maximum lateralacceleration of the host vehicle 101. If the needed lateral accelerationexceeds the maximum lateral acceleration, the computer 105 can set theSTN to 1.

The ATN is a measure of a needed longitudinal acceleration to allow thehost vehicle 101 to accelerate and pass the target 200. As describedabove for the BTN and the STN, the computer 105 can determine a neededacceleration to allow the host vehicle 101 to pass the target 200 and amaximum available acceleration of the host vehicle 101. The ATN can bethe ratio of the needed longitudinal acceleration to the maximumlongitudinal acceleration of the host vehicle 101. If the neededlongitudinal acceleration exceeds a maximum longitudinal acceleration,the computer 105 can set the ATN to 1. The computer 105 may determinethe STN, BTN, and/or ATN to produce a respective threat number TN foreach target 200.

The computer 105 can determine the threat number TN based on thepredicted trajectories of the host vehicle 101 and the target 200. Thatis, based on the position, velocity, acceleration, and turn rate of thehost vehicle 101 and the target 200, the computer 105 can determine thethreat number TN for the target 200. In one non-limiting example, thethreat number can be a ratio of a required deceleration to stop thetarget 200 prior to entering the host vehicle path 210 h (i.e., a“zero-range” deceleration) to a predetermined maximum deceleration ofthe target 200.

The computer 105 can actuate one or more vehicle components 120 based onthe threat number TN, e.g., when the threat number TN is above apredetermined threat number threshold. The computer 105 can actuate oneor more components 120 based on a comparison of the threat number to aplurality of thresholds. The thresholds can be determined as, e.g.,specifications from a manufacturer, results of simulation testing ofvirtual host vehicle and virtual target vehicle trajectories, empiricaltesting of vehicle components 120 during a collision test, etc. Forexample, if the threat number TN is above 0.7, the computer 105 canactuate a brake 120 to decelerate the host vehicle 101, e.g., at −6.5meters per second squared (m/s²). In another example, if the threatnumber TN is above 0.4 but less than or equal to 0.7, the computer 105can actuate the brake 120 to, e.g., a deceleration of −2.0 m/s². Inanother example, if the threat number TN is greater than 0.2 but lessthan or equal to 0.4, the computer 105 can display a visual warning on ahost vehicle 101 human-machine interface and/or play an audio warningover a speaker.

The computer 105 can perform a collision avoidance action based on thethreat number. In this context, a “collision avoidance action” is anaction including actuation of one or more components 120 to avoid and/ormitigate a collision with the target vehicle 200. The collisionavoidance action can include, e.g., providing a forward collisionwarning on a display screen in an interior of the vehicle 101, actuatinga brake 120 to stop the host vehicle 101, actuating a propulsion 120 toavoid the target vehicle 200, actuating a steering component 120 toavoid the target vehicle 200, etc. The computer 105 can suppress thecollision avoidance action upon determining that the target vehicle 200is not likely to collide with the vehicle 101, i.e., the likelihood ofthe target vehicle 200 to collide with the host vehicle 101 would notrequire collision mitigation or avoidance. In this context, to“suppress” a collision avoidance action is to prevent one or morecomponents 120 from performing the collision avoidance action. Forexample, the computer 105 can prevent a human-machine interface (HMI)such as a display from displaying a warning indicating a forwardcollision. In another example, the computer 105 can prevent a steeringmotor from receiving an instruction to steer away from the targetvehicle 200.

In the example of FIG. 2, the target vehicle 200 is occluded from thehost vehicle 101. The intersection can include an object 205 that canprevent sensors 110 in the host vehicle 101 from detecting the targetvehicle 200. For example, the object 205 can be a building, a utilitypillar, etc. As used herein, the target vehicle 200 is “occluded” whenthe sensors 110 in the host vehicle 101 do not detect the target vehicle200 and the computer 105 of the host vehicle 101 receives the messagefrom the target vehicle 200. That is, the computer 105 has received inthe message data stating the existence of the target vehicle 200 butdoes not detect the target vehicle 200 with the sensors 110. The object205 can block the sensors 110 from collecting data 115, e.g., blocking afield of vision of an image sensor 110, reflecting ultrasonic wavesand/or radar waves, blocking lasers of a LIDAR 110, etc. When thecomputer 105 determines that the target vehicle 200 is occluded, thecomputer 105 can perform the collision avoidance action. When thecomputer 105 determines that the target vehicle 200 is not occluded,i.e., when the computer 105 identifies the target vehicle 200 with data115 from the sensors 110 and data 115 from the message, the computer 105can determine to suppress the collision avoidance action. That is, whenthe target vehicle 200 is not occluded, an operator of the host vehicle101 can perform collision avoidance, and the computer 105 can determineto suppress the collision avoidance action to allow the operator toavoid the target vehicle 200. Alternatively, the computer 105 candetermine to allow the collision avoidance action based on data 115collected from the sensors 110 when the target vehicle 200 is notoccluded.

FIG. 3 is a plan view of the host vehicle 101, the target vehicle 200,and a second target vehicle 300. The computer 105 can receive themessage from the target vehicle 200, as described above. The secondtarget vehicle 300 may not send a message to the computer 105. Thus, thecomputer 105 may receive a message from a target vehicle 200 and detectone or more target vehicles 200, 300 with data 115 from one or moresensors 110. That is, the computer 105 may not immediately recognizewhich target vehicle 200, 300 sent the message.

The computer 105 can collect data 115 about the second target vehicle300 with one or more sensors 110. Because the second target vehicle 300is at a closer distance to the host vehicle than the first targetvehicle 200, the second target vehicle 300 is more likely to collidewith the host vehicle 101 than the first target vehicle 200. Thus, thecomputer 105 can collect data 115 about the second target vehicle 300before collecting data 115 about the first target vehicle 200.

The computer 105 can determine whether the first target vehicle 200 andthe second target vehicle 300 are a same vehicle. The computer 105 cancompare the data 115 from the message, which the computer 105 identifiesas being received from the first target vehicle 200, to the data 115collected by the sensors 110, which the computer 105 identifies ascollected from the second target vehicle 300. If the data 115 receivedfrom the first target vehicle 200 substantially match the data 115collected about the second target vehicle 300, the computer 105 candetermine that the first target vehicle 200 and the second targetvehicle 300 are the same vehicle. For example, from data 115, thecomputer 105 can determine whether the speed of the first target vehicle200 is within a speed threshold of the speed of the second targetvehicle 300, e.g., 3 miles per hour. If the speeds (i.e., a differencebetween them) are within the speed threshold, the computer 105 candetermine that the first target vehicle 200 and the second targetvehicle 300 are the same vehicle.

In another example, the computer 105 can compare a difference ofpositions of the first target vehicle 200 and the second target vehicle300, from position data 115, to a position threshold, e.g., 2 squaremeters, and if the positions are within the position threshold, thecomputer 105 can determine that the first target vehicle 200 and thesecond target vehicle 300 are the same vehicle.

In yet another example, the computer 105 can compare route history data115 of the first target vehicle 200 and route history data 115 of thesecond target vehicle 300 to determine if a difference of routes iswithin a route threshold, e.g., the routes are within 2 meters over theprevious 50 meters. If the routes are within the route threshold, thecomputer 105 can determine that the first target vehicle 200 and thesecond target vehicle 300 are a same vehicle. In this context, “routehistory” data are travel path data indicating paths of travel of thetarget vehicles 200, 300. In yet another example, the computer 105 canuse a combination of the data 115, e.g., the speed data 115, theposition data 115, and/or the route history data 115, to determinewhether the first target vehicle 200 and the second target vehicle 300are the same vehicle.

In yet another example, the computer 105 can compare a difference insizes, based on data 115, of the first target vehicle 200 and the secondtarget vehicle 300 to a size threshold, e.g., 20 cm. That is, a vehicle“size” is a set of dimensions, such as can be determined from sensor 110data 115, indicating a length, a width, and a height of the vehicle. Ifeach of the length, the width, and the height of the first targetvehicle 200 are within the size threshold of the length, the width, andthe height of the second target vehicle 300, the computer 105 candetermine that the first target vehicle 200 and the second targetvehicle 300 are the same vehicle. If any one of the length, the width,or the height of the first target vehicle 200 differs from therespective length, the width, or the height of the second target vehicle300, the computer 105 can determine that the first target vehicle 200and the second target vehicle 300 are not the same vehicle.

If the first target vehicle 200 and the second target vehicle 300 arenot a same vehicle, the computer 105 can determine the threat number forthe second target vehicle 300. As described above, the computer 105 candetermine the threat number TN as one of a brake threat number BTN, asteering threat number STN, or an acceleration threat number ATN. Thecomputer 105 can determine the threat number TN with a threat numberalgorithm as described above. If the threat number of the second targetvehicle 300 is above the threat number threshold described above, thecomputer 105 can determine that the second target vehicle 300 is agreater threat than the first target vehicle 200 and the computer 105can suppress the collision avoidance action associated with the firsttarget vehicle 200. That is, the computer 105 focuses on the secondtarget vehicle 300 instead of the first target vehicle 200 and/or allowsthe operator to perform collision avoidance for the second targetvehicle 300. Alternatively, if the threat number of the second targetvehicle 300 is below the threat number threshold, the computer 105 canallow the collision avoidance action for the first target vehicle 200.Yet further alternatively, the computer 105 can compare the threatnumber of the second target vehicle 300 to a second threat numberthreshold that is different than the threat number threshold. Becausethe second target vehicle 300 may be at a closer distance to the hostvehicle 101 than the first target vehicle 200, an operator of the hostvehicle 101 may see the second target vehicle 300 and perform collisionmitigation and avoidance for the second target vehicle 300 than for thefirst target vehicle 200. Thus, the second threat number threshold canbe greater than the threat number threshold to account for theoperator's awareness of the second target vehicle 300.

FIG. 4 is a plan view of a barrier 400 between the host vehicle 101 andthe target vehicle 200. The barrier 400 can be a structure thatseparates a roadway lane from another roadway lane to prevent vehiclesfrom crossing between the roadway lanes. For example, the barrier 400can be a concrete wall separating two roadway lanes. When there is abarrier 400 between the host vehicle 101 and the target vehicle 200, thetarget vehicle 200 is typically unable to cross through the barrier 400and collide with the host vehicle 101, and the computer 105 can suppressa collision avoidance action for the target vehicle 200.

Upon determining that the threat number of the target vehicle 200 isabove the threat number threshold, the computer 105 can collect data 115with one or more sensors 110 to determine whether there is a barrier 400between the host vehicle 101 and the target vehicle 200. The computer105 can actuate a camera 110 to collect image data 115 of objects nearthe host vehicle 101. The computer 105 can apply a conventional imagedetection technique, e.g., Canny edge detection, to identify the barrier400 between the host vehicle 101 and the target vehicle 200. In anotherexample, the computer 105 can refer to a high-resolution geo-coordinate(e.g., GPS) map, that can include stored features such as barriers 400.

FIG. 5 is a plan view of the target vehicle 200 at a different elevationthan the host vehicle 101. In this context, the “elevation” of thetarget vehicle 200 is the vertical distance of the target vehicle 200relative to the host vehicle 101. When the target vehicle 200 is at adifferent elevation than the host vehicle 101, the target vehicle 200 isnot likely to collide with the host vehicle 101, and the computer 105can suppress a collision avoidance action for the target vehicle 200.For example, if the target vehicle 200 is on an overpass that extendsover a roadway on which the host vehicle 101 is traveling, the targetvehicle 200 is at a different elevation than the host vehicle 101 and isnot likely to collide with the host vehicle 101.

The host vehicle 101 can determine the elevation of the target vehicle200. For example, the host vehicle 101 can actuate a camera 110 tocollect image data 115 of the target vehicle 200 and, using aconventional distance determining technique, identify a verticaldistance of the target vehicle 200 relative to the host vehicle 101. Ifthe identified vertical distance is above a threshold, e.g., a height ofthe host vehicle 101, the computer 105 can determine that the targetvehicle 200 is at an elevation that is not likely to collide with thehost vehicle 101, and the computer 105 can suppress a collisionavoidance action for the target vehicle 200. That is, the computer 105can compare the elevation of the target vehicle 200 to an elevationthreshold. The elevation threshold can be a vertical distance abovewhich the target vehicle 200 would not collide with the host vehicle 101even if the host vehicle 101 and the target vehicle 200 were at the samelateral and longitudinal position. The elevation threshold can be twicethe height of a typical vehicle 101, e.g., 3 meters. If the elevation ofthe target vehicle 200 differs from the elevation of the host vehicle101 by more than the elevation threshold, the computer 105 can determinethat the target vehicle 200 is not likely to collide with the hostvehicle 101 and suppress the collision avoidance action. Alternatively,the host vehicle 101 can receive the elevation of the target vehicle 200from the message from the target vehicle 200.

In another example, the computer 105 can actuate the camera 110 tocollect image data 115 of an environment around the target vehicle 200and, using a conventional image recognition technique, identify anoverpass or bridge on which the target vehicle 200 is traveling andbelow which the host vehicle 101 will travel. That is, if the targetvehicle 200 is on infrastructure above the roadway on which the hostvehicle 101 is traveling (e.g., an overpass, a bridge, etc.), thecomputer 105 can determine that the target vehicle 200 is at anelevation that is not likely to collide with the host vehicle 101, andthe computer 105 can suppress a collision avoidance action for thetarget vehicle 200.

In yet another example, the host vehicle 101 can identify globalposition coordinates of the target vehicle 200 and compare thecoordinates to a high-resolution map that includes features such as anoverpass and a bridge. If the high-resolution map indicates that thetarget vehicle 200 is on infrastructure above the host vehicle 101, thecomputer 105 can determine that target vehicle 200 is at an elevationthat is not likely to collide with the host vehicle 101, and thecomputer 105 can suppress a collision avoidance action for the targetvehicle 200. Alternatively, if the computer 105 determines that theelevation of the target vehicle 200 is below the host vehicle 101 by thethreshold (e.g., with image data 115 as described above), the computer105 can determine that target vehicle 200 is at an elevation that is notlikely to collide with the host vehicle 101, and the computer 105 cansuppress a collision avoidance action for the target vehicle 200.

The computer 105 can arbitrate the message from the first target vehicle200 and the data 115 about the second target vehicle 300. In thiscontext, to “arbitrate” is to collect more than one set of data 115 andto perform a collision avoidance action according to one of the sets ofdata 115 and to suppress collision avoidance actions according to theother set of data 115, i.e., to select to use a first set of data andnot a second set of data. The computer 105 can arbitrate the collisionavoidance action directed toward the first target vehicle 200 and asecond collision avoidance action directed toward the second targetvehicle 300 based on an order of receipt of the message and the data115. That is, if the computer 105 receives the message form the firsttarget vehicle 200 before collecting data 115 about the second targetvehicle 300, the computer 105 can perform the collision avoidance actionto avoid and/or mitigate a collision with the first target vehicle 200and suppress the second collision avoidance action. If the computer 105collects the data 115 about the second target vehicle 300 beforereceiving the message from the first target vehicle 200, the computer105 can perform the second collision avoidance action to avoid and/ormitigate a collision with the second target vehicle 300 and suppress thecollision avoidance action.

FIGS. 6A-6B are a block diagram of an example process 600 for collisionmitigation and avoidance. The process 600 begins in a block 605, inwhich a computer 105 of a host vehicle 101 receives a message from afirst target vehicle 200. As described above, the message from the firsttarget vehicle 200 can include data 115 about the first target vehicle200, e.g., speed, position, route history, etc.

Next, in a block 605, the computer 105 determines a threat number forthe first target vehicle 200. As described above, the computer 105 canuse the data 115 of the message to determine a threat number TN of thefirst target vehicle 200 that is a prediction of whether a specifictarget 200 will intersect or collide with the host vehicle 101. Forexample, the computer 105 can determine a brake threat number BTN, asteering threat number STN, and/or an acceleration threat number ATN.

Next, in a block 615, the computer 105 determines whether the threatnumber of the first target vehicle 200 exceeds a threat numberthreshold. As described above, the threat number threshold can be avalue determined based a likelihood of a collision between the hostvehicle 101 and the first target vehicle 200. If the threat number ofthe first target vehicle 200 exceeds the threat number threshold, theprocess 600 continues in a block 620. Otherwise, the process 600continues in a block 670.

In the block 620, the computer 105 actuates one or more sensors 110 todetect a second target vehicle 300. As described above, the computer 105can collect data 115 with the sensors 110 of nearby vehicles 300. Forexample, the computer 105 can collect image data 115 with a camera 110and detect the second target vehicle 300 with a conventionalimage-recognition algorithm, e.g., Canny edge detection.

Next, in a block 625, the computer 105 determines whether the firsttarget vehicle 200 and the second target vehicle 300 are a same vehicle.As described above, the computer 105 can compare at least one of speed,position, and/or route history data 115 received from the first targetvehicle 200 and detected for the second target vehicle 300 to determinewhether the first target vehicle 200 and the second target vehicle 300are the same vehicle. That is, the computer 105 determines whether thevehicle that sent the message over the network 125 is the same vehicleas the vehicle detected by the sensors 110. If the speed, position,and/or route history data 115 are within respective thresholds, asdescribed above, the computer 105 can determine that the first targetvehicle 200 and the second target vehicle 300 are the same vehicle. Ifthe first target vehicle 200 and the second target vehicle 300 are thesame vehicle, the process 600 continues in a block 630. Otherwise, theprocess 600 continues in a block 635. Alternatively, the process 600 canproceed to the block 630 when the computer 105 detect no second targetvehicle 300, i.e., the computer 105 does not detect any vehicles thatcould have sent the message.

In the block 630, the computer 105 determines whether there is an object205 occluding the first target vehicle 200 from the host vehicle 101. Asdescribed above, the computer 105 can actuate one or more sensors 110 todetect the first target vehicle 200. The computer 105 may not detect thefirst target vehicle 200 if an object 205 occludes the first targetvehicle 200. The object 205 can block the sensors 110 from collectingdata 115, e.g., blocking a field of vision of an image sensor 110,reflecting ultrasonic waves and/or radar waves, blocking lasers of aLIDAR 110, etc.

If the computer 105 detects an occluding object 205, the process 600continues in a block 660. Otherwise, the process 600 continues in ablock 665.

In the block 635, the computer 105 determines a threat number TN for thesecond target vehicle 300. As described above, the computer 105 candetermine the threat number TN based on the data 115 collected about thesecond target vehicle 300 from the sensors 110. The threat number TN canbe, e.g., a brake threat number BTN, a steering threat number STN, anacceleration threat number ATN, etc.

Next, in a block 640, the computer 105 determines whether the threatnumber TN for the second target vehicle 300 exceeds a second threatnumber threshold. As described above, the second threat number thresholdcan be a value determined based a likelihood of a collision between thehost vehicle 101 and the second target vehicle 300. The second threatnumber threshold can differ from the threat number threshold for thefirst target vehicle 200. For example, the second target vehicle 300 maybe closer to the host vehicle 101 than the first target vehicle 200 andthe operator of the host vehicle 101 may see the second target vehicle300 and perform collision mitigation and avoidance. Thus, because theoperator may see the second target vehicle 300, the second threat numberthreshold may be higher than the threat number threshold. Alternatively,the second threat number threshold can be the same as the threat numberthreshold. If the second threat number exceeds the second threat numberthreshold, the process 600 continues in the block 665. Otherwise, theprocess 600 continues in a block 645.

In the block 645, the computer 105 determines whether there is a barrier400 detected between the host vehicle 101 and the first target vehicle200. As described above, when there is a barrier 400 between the hostvehicle 101 and the first target vehicle 200 (e.g., a concrete wallseparating two roadway lanes), the first target vehicle 200 is notlikely to collide with the host vehicle 101. The computer 105 can detectthe barrier 400 based on data 115 collected with one or more sensors110. For example, the computer 105 can collect a plurality of imagesand, using a conventional image-recognition technique, e.g., Canny edgedetection, can determine whether the images indicate that there is abarrier 400 between the host vehicle 101 and the first target vehicle200. If the computer 105 detects a barrier 400 between the host vehicle101 and the first target vehicle 200, the process 600 continues in theblock 665. Otherwise, the process 600 continues in a block 650.

In the block 650, the computer 105 determines whether the elevation ofthe first target vehicle 200 exceeds an elevation threshold. Asdescribed above, if the elevation of the first target vehicle 200 ishigher or lower than the host vehicle 101, the first target vehicle 200may not be likely to collide with the host vehicle 101. For example, ifthe first target vehicle 200 is on an overpass above the host vehicle101, the first target vehicle 200 is not likely to collide with the hostvehicle 101. If the elevation of the first target vehicle 200 exceedsthe elevation threshold, the process 600 continues in the block 665.Otherwise, the process 600 continues in a block 655.

In the block 655, the computer 105 arbitrates the message from the firsttarget vehicle 200 and the data 115 collected about the second targetvehicle 300 to determine which of the message and the data 115 werereceived first. That is, if the computer 105 receives the message fromthe first target vehicle 200 before collecting data 115 about the secondtarget vehicle 300, the computer 105 can perform a collision avoidanceaction for the first target vehicle 200. If the computer 105 determinesthat the message from the first target vehicle 200 was received first,the process 600 continues in a block 660. Otherwise, the process 600continues in the block 665.

In the block 660, the computer 105 allows the collision avoidance actionin accordance with the message from the first target vehicle 200. Thatis, the computer 105 performs the collision avoidance action based onthe data in the message from the first target vehicle 200. For example,the computer 105 can provide a forward collision warning on an HMI in aninterior of the host vehicle 101. In another example, the computer 105can actuate a brake to slow the host vehicle 101 to allow the firsttarget vehicle 200 to pass the host vehicle 101.

In the block 665, the computer 105 suppresses the collision avoidanceaction. As described above, because the first target vehicle 200 is notlikely to collide with the host vehicle 101, the computer 105 suppressesactions typically performed to mitigate and/or avoid the collision. Forexample, the computer 105 can suppress a forward collision warning fromappearing on the HMI.

In the block 670, the computer 105 determines whether to continue theprocess 600. For example, the computer 105 can determine to continue theprocess 600 upon receiving another message from a target vehicle 200. Ifthe computer 105 determines to continue, the process 600 returns to theblock 605. Otherwise, the process 600 ends.

As used herein, the adverb “substantially” modifying an adjective meansthat a shape, structure, measurement, value, calculation, etc. maydeviate from an exact described geometry, distance, measurement, value,calculation, etc., because of imperfections in materials, machining,manufacturing, data collector measurements, computations, processingtime, communications time, etc.

Computing devices discussed herein, including the computer 105 and theserver 130 include processors and memories, the memories generally eachincluding instructions executable by one or more computing devices suchas those identified above, and for carrying out blocks or steps ofprocesses described above. Computer executable instructions may becompiled or interpreted from computer programs created using a varietyof programming languages and/or technologies, including, withoutlimitation, and either alone or in combination, Java™, C, C++, Python,Visual Basic, Java Script, Perl, HTML, etc. In general, a processor(e.g., a microprocessor) receives instructions, e.g., from a memory, acomputer readable medium, etc., and executes these instructions, therebyperforming one or more processes, including one or more of the processesdescribed herein. Such instructions and other data may be stored andtransmitted using a variety of computer readable media. A file in thecomputer 105 is generally a collection of data stored on a computerreadable medium, such as a storage medium, a random access memory, etc.

A computer readable medium includes any medium that participates inproviding data (e.g., instructions), which may be read by a computer.Such a medium may take many forms, including, but not limited to, nonvolatile media, volatile media, etc. Non volatile media include, forexample, optical or magnetic disks and other persistent memory. Volatilemedia include dynamic random access memory (DRAM), which typicallyconstitutes a main memory. Common forms of computer readable mediainclude, for example, a floppy disk, a flexible disk, hard disk,magnetic tape, any other magnetic medium, a CD ROM, DVD, any otheroptical medium, punch cards, paper tape, any other physical medium withpatterns of holes, a RAM, a PROM, an EPROM, a FLASH EEPROM, any othermemory chip or cartridge, or any other medium from which a computer canread.

With regard to the media, processes, systems, methods, etc. describedherein, it should be understood that, although the steps of suchprocesses, etc. have been described as occurring according to a certainordered sequence, such processes could be practiced with the describedsteps performed in an order other than the order described herein. Itfurther should be understood that certain steps could be performedsimultaneously, that other steps could be added, or that certain stepsdescribed herein could be omitted. For example, in the process 600, oneor more of the steps could be omitted, or the steps could be executed ina different order than shown in FIGS. 6A-6B. In other words, thedescriptions of systems and/or processes herein are provided for thepurpose of illustrating certain embodiments, and should in no way beconstrued so as to limit the disclosed subject matter.

Accordingly, it is to be understood that the present disclosure,including the above description and the accompanying figures and belowclaims, is intended to be illustrative and not restrictive. Manyembodiments and applications other than the examples provided would beapparent to those of skill in the art upon reading the abovedescription. The scope of the invention should be determined, not withreference to the above description, but should instead be determinedwith reference to claims appended hereto and/or included in a nonprovisional patent application based hereon, along with the full scopeof equivalents to which such claims are entitled. It is anticipated andintended that future developments will occur in the arts discussedherein, and that the disclosed systems and methods will be incorporatedinto such future embodiments. In sum, it should be understood that thedisclosed subject matter is capable of modification and variation.

The article “a” modifying a noun should be understood as meaning one ormore unless stated otherwise, or context requires otherwise. The phrase“based on” encompasses being partly or entirely based on.

What is claimed is:
 1. A system, comprising a computer including aprocessor and a memory, the memory storing instructions executable bythe processor to, in a host vehicle: determine a threat number for afirst target vehicle based on data received from the first targetvehicle indicating at least one of a position, speed, or route historyof the first target vehicle; identify a second target vehicle based ondata collected with one or more host vehicle sensors in response todetermining that the threat number of the first target vehicle exceeds athreshold; determine that the first target vehicle and the second targetvehicle are not a same vehicle; and then, upon determining that thefirst target vehicle and the second target vehicle are not the samevehicle and that at least one of (a) a barrier is detected between thehost vehicle and the first target vehicle or (b) that a threat number ofthe second target vehicle exceeds a second threshold, suppress acollision avoidance action.
 2. The system of claim 1, wherein theinstructions further include instructions to determine that the firsttarget vehicle and the second target vehicle are not the same vehiclewhen data from the sensors indicating at least one of a size, position,and speed differs from corresponding received data by a threshold. 3.The system of claim 1, wherein the instructions further includeinstructions to suppress the collision avoidance action upon determiningthat a difference between a vertical position of the first targetvehicle and a vertical position of the host vehicle exceeds a threshold.4. The system of claim 1, wherein the instructions further includeinstructions to suppress the collision avoidance action upon determiningthat the first target vehicle is on a bridge above the host vehicle. 5.The system of claim 1, wherein the instructions further includeinstructions to allow the collision avoidance action upon failing todetect the second target vehicle with the vehicle sensors.
 6. The systemof claim 1, wherein the instructions further include instructions toallow the collision avoidance action upon determining that the threatnumber of the second target vehicle is below a second threshold.
 7. Thesystem of claim 1, wherein the instructions further include instructionsto arbitrate the collision avoidance action and a second collisionavoidance action, the second collision avoidance action directed towardthe second target vehicle, based on a receipt order of the received datafrom the first target vehicle and the sensor data identifying the secondtarget vehicle.
 8. The system of claim 1, wherein the instructionsfurther include instructions to suppress the collision avoidance actionupon identifying that the first target vehicle and the second targetvehicle are the same vehicle and detecting no occluding object betweenhost vehicle and the first target vehicle.
 9. The system of claim 8,wherein the instructions further include instructions to allow thecollision avoidance action upon identifying an occluding object betweenthe host vehicle and the first target vehicle.
 10. The system of claim1, wherein the instructions further include instructions to suppress thecollision avoidance action upon determining that the second targetvehicle is closer to the host vehicle than the first target vehicle. 11.A method, comprising: determining a threat number for a first targetvehicle based on data received from the first target vehicle indicatingat least one of a position, speed, or route history of the first targetvehicle; identifying a second target vehicle based on data collectedwith one or more host vehicle sensors in response to determining thatthe threat number of the first target vehicle exceeds a threshold;determining that the first target vehicle and the second target vehicleare not a same vehicle; and then, upon determining that the first targetvehicle and the second target vehicle are not the same vehicle and thatat least one of (a) a barrier is detected between a host vehicle and thefirst target vehicle or (b) that a threat number of the second targetvehicle exceeds a second threshold, suppressing a collision avoidanceaction.
 12. The method of claim 11, further comprising determining thatthe first target vehicle and the second target vehicle are not the samevehicle when data from the sensors indicating at least one of a size,position, and speed differs from corresponding received data by athreshold.
 13. The method of claim 11, further comprising allowing thecollision avoidance action upon determining that the threat number ofthe second target vehicle is below the second threshold.
 14. The methodof claim 11, further comprising arbitrating the collision avoidanceaction and a second collision avoidance action, the second collisionavoidance action directed toward the second target vehicle, based on areceipt order of the received data from the first target vehicle and thesensor data identifying the second target vehicle.
 15. The method ofclaim 11, further comprising suppressing the collision avoidance actionupon identifying that the first target vehicle and the second targetvehicle are the same vehicle and detecting no occluding object betweenhost vehicle and the first target vehicle.
 16. A system, comprising: ahost vehicle sensor; means for determining a threat number for a firsttarget vehicle based on data received from the first target vehicleindicating at least one of a position, speed, or route history of thefirst target vehicle; means for identifying a second target vehiclebased on data collected with the host vehicle sensor in response todetermining that the threat number of the first target vehicle exceeds athreshold; means for determining that the first target vehicle and thesecond target vehicle are not a same vehicle; and means for suppressinga collision avoidance action upon determining that the first targetvehicle and the second target vehicle are not the same vehicle and thatat least one of (a) a barrier is detected between a host vehicle and thefirst target vehicle or (b) that a threat number of the second targetvehicle exceeds a second threshold.
 17. The system of claim 16, furthercomprising means for determining that the first target vehicle and thesecond target vehicle are not the same vehicle when data from thesensors indicating at least one of a size, position, and speed differsfrom corresponding received data by a threshold.
 18. The system of claim16, further comprising means for allowing the collision avoidance actionupon determining that the threat number of the second target vehicle isbelow the second threshold.
 19. The system of claim 16, furthercomprising means for arbitrating the collision avoidance action and asecond collision avoidance action, the second collision avoidance actiondirected toward the second target vehicle, based on a receipt order ofthe received data from the first target vehicle and the sensor dataidentifying the second target vehicle.
 20. The system of claim 16,further comprising means for suppressing the collision avoidance actionupon identifying that the first target vehicle and the second targetvehicle are the same vehicle and detecting no occluding object betweenhost vehicle and the first target vehicle.